GEAC 2025

Emerging Technology (AI and Quantum Technologies)

Closing the gender gap in emerging technology (tech) is not only a matter of equity—it is a driver of innovation, economic growth, and democratic resilience. While ground has been lost in traditional information technologies, emerging fields like artificial intelligence (AI) and quantum computing present a second chance—a unique opportunity to reset the course and ensure women are part of shaping the future. GEAC calls on the G7 to build on its previous commitments in the areas of science, technology, engineering, and mathematics (STEM) and tech-facilitated gender-based violence to address the related issues in AI and quantum technologies.

Recommendations

Increase women’s participation in AI and quantum technologies design and governance

Establish targets for women’s representation in AI and quantum technologies governance bodies. Currently, women represent only 22% in AI and 14% in executive positions:[1]

For example, establish G7-wide standards with a goal of 30% women in executive, board, and governance roles in AI and quantum technologies sectors by 2028.
Tie government procurement and public funding for AI and quantum technologies innovation to disclosing and meeting those targets.

Promote policies that support gender diversity in leadership positions within AI and quantum technologies organizations and businesses, including anti-discrimination and gender-responsive promotion polices.
Support AI-adoption by women-led small and medium sized enterprises (SMEs):

Support women-led SMEs in adopting AI through training, resources, and tax incentives.
Offer targeted digital adoption grants to 250,000 women-led small businesses across the G7 by 2030.

Create a G7 women-in-tech innovation fund:

Deploy US$1 billion across G7 countries in seed capital and scale-up grants for women-founded AI and quantum technologies companies.
Finance this fund through public–private partnerships with venture capital funds, accelerators, and national development banks.

Foster gender-inclusive STEM education from an early age:

Integrate inclusive and gender-sensitive pedagogical approaches to STEM in school curriculums from primary school onward.
Set targets for girls pursuing science programs and tracks in high school: 50% for math and physics and 30% for computer science.

Promote early exposure and access to AI and quantum technologies careers: Launch targeted programs for girls, including mentorship, scholarships, internships, and career orientation—building on successful initiatives like the French program TechPourToutes.
Scale up successful initiatives, such as the Cercle InterElles “Women & AI” Pledge and the French Laboratory for Women Rights Online, and share best practices, such as those listed in the report Towards Substantive Equality in Artificial Intelligence by the Global Partnership on Artificial Intelligence.
Strengthen accountability, collect, and publish gender and/or sex-disaggregated data, for example through the G7 Dashboard on Gender Gaps, to measure women’s participation and leadership in the AI and quantum technologies sectors and their access to digital tools.

Protect women and girls from AI-facilitated violence and harassment such as pornographic deepfakes

Promote global standards and adopt legislation or enforce human rights law to eliminate AI-facilitated violence such as pornographic deepfakes, including from social media platforms.
Ensure accountability for the algorithms and platforms that amplify or enable the spread of extreme pornography and misogyny, including through transparency requirements, independent audits, and regulatory oversight.
Establish support systems, including legal and psychological assistance, for victims of AI-facilitated violence.
Increase awareness among youth—girls and boys—through advocacy campaigns online and in schools.

Ensure AI systems are bias free

Identify the biases, consider how data is collected, and promote trustworthy data collection and data-sharing across countries.
Mandate gender impact assessments for publicly deployed AI systems (including in hiring, health triage, and social services).
Adopt G7-wide algorithmic audit standards (for example, building on Canada’s Algorithmic Impact Assessment tool).

Rationale

Most AI solutions are developed by men. This reflects the low rate of women graduates in STEM education, which in the G7 countries is at 33%.[2] Over the past decade, progress has been minimal, while demand for tech talent is accelerating. Accurate and comprehensive data is essential for understanding and addressing gender gaps in emerging technologies.

The gender gap in the AI workforce contributes to the existing biases, as technology designed and built mostly by men is susceptible to being skewed to represent their individual experiences.

Women’s involvement and leadership in AI and quantum technologies brings diverse perspectives, increases the likelihood that gender-based biases are addressed, and helps drive innovation and economic growth. Moreover, AI and quantum technologies cannot remain concentrated in large firms and elite labs. Scaling access to women entrepreneurs, especially in non-tech sectors (such as care, climate, and creative industries) will expand inclusive growth. Bridging the gender digital divide could save US$500 billion globally in the coming years.[3]

Online harms, including AI-generated violence and harassment like deep fakes primarily target women and girls, thereby undermining their safety and participation in digital spaces and public life. Between 96% and 98% of online deepfake videos are pornographic and nonconsensual, with 99% of them targeting women.[4]

Data bias in AI is widespread, with one study indicating that 44% of AI systems show stereotypes and gender biases[5] that exacerbate existing inequalities. For example, AI-driven recruitment systems may reject women candidates because their names are not male-associated or inadvertently match female applicants to lower-paying or less-prestigious positions. These biases can have devastating effects in, for example, the health sector, where AI systems often misdiagnose women because they’re trained on mostly male data.

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Footnotes

[1] Pal, Siddi; Ruggero Marino Lazzaroni; and Paula Mendoza. AI's Missing Link: The Gender Gap in the Talent Pool. (2024) ↩

[2] OECD. Education at a Glance: OECD Indicators. (2023) ↩

[3] UN Women and DESA. Op. Cit. ↩

[4] A 2019 study by DeepTrace found that 96% of online deepfake videos were pornographic and nonconsensual. A 2023 study by Home Security Heroes found that deepfake porn makes up 98% of all deepfake videos online, with 99% of them targeting women. ↩

[5] Smith, Genevieve, and Ishita Rustagi. “When Good Algorithms Go Sexist: Why and How to Advance AI Gender Equity.” Stanford Social Innovation Review. (2021) ↩

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Source: Official website of Canada's 2025 G7 presidency

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	Please send comments to: g7@utoronto.ca This page was last updated April 12, 2026.